df2.duplicated()#检查重复值 以Boolean形式进行输出展示 df2.duplicated().sum()#打印有多少重复值 df2[df2.duplicated()]#打印重复值 df2[df2.duplicated()==False]#打印非重复值 df2.drop_duplicates()#删除重复值(此操作并不是在数据源本身进行删除操作) df2.drop_duplicates(inplace=True)#删除重复值(...
"""new_val = val.replace('%','')returnfloat(new_val) /100df_2 = pd.read_csv("sales_data_types.csv",dtype={"Customer_Number":"int"},converters={"2016":convert_currency,"2017":convert_currency,"Percent Growth":convert_percent,"Jan Units":lambdax:pd.to_numeric(x,errors="coerce"),...
(new_val) / 100 df_2 = pd.read_csv("sales_data_types.csv",dtype={"Customer_Number":"int"},converters={ "2016":convert_currency, "2017":convert_currency, "Percent Growth":convert_percent, "Jan Units":lambda x:pd.to_numeric(x,errors="coerce"), "Active":lambda x: np.where(x==...
pd.to_datetime(df[['Month', 'Day', 'Year']]) Output: 0 2015-01-10 1 2014-06-15 2 2016-03-29 3 2015-10-27 4 2014-02-02 dtype: datetime64[ns] 该函数将列组合成一系列适当的 datateime64 dtype,很方便 最后,我们把上面处理代码都放到一起 df_2 = pd.read_csv("sales_data_types....
df = pd.read_csv("sales_data_types.csv") Output: 乍一看,数据好像还不错,所以我们可以尝试做一些操作来分析数据。 让我们尝试将 2016 年和 2017 年的销售额相加: df['2016'] + df['2017'] Output: 0 $125,000.00$162500.00 1 $920,000.00$101,2000.00 ...
defreduce_memory_usage(df, verbose=True):numerics= ["int8","int16","int32","int64","float16","float32","float64"]start_mem = df.memory_usage.sum /1024**2forcol in df.columns:col_type = df[col].dtypesifcol_type in numerics:c_min = df[col].minc_max = df[col].maxifstr(...
df = pd.read_csv("data/sales_data_types.csv") df.head() 1. 2. 3. 4. 5. 输出结果为: 数据类型相关操作: 1. 查看DataFrame所有列的类型: 通过df.dtypes或者是,即可查看df对象的类型。输入df.dtypes输出结果如下: ...
df= pd.read_csv("https:///chris1610/pbpython/blob/master/data/sales_data_types.csv?raw=True") 1. 2. 3. 4. 然后我们查看每个字段的数据类型: 数据类型问题如下: Customer number应该是int64,不应该是float64 2016和2017两个字段是object字符串,但我们应该将其转换为float64或者int64 ...
StructField("age",IntegerType(),True)\])df=spark.createDataFrame(data=data,schema=schema) PySpark 可以通过如下代码来检查数据类型: 代码语言:python 代码运行次数:0 运行 AI代码解释 df.dtypes# 查看数据类型df.printSchema() 💡 读写文件 Pandas 和 PySpark 中的读写文件方式非常相似。 具体语法对比如下...
df_2 = pd.read_csv("sales_data_types.csv", dtype={'Customer Number': 'int'}, converters={'2016': convert_currency, '2017': convert_currency, 'Percent Growth': convert_percent, 'Jan Units': lambda x: pd.to_numeric(x, errors='coerce'), 'Active': lambda x: np.where(x == "Y...